Most face recognition approaches either assume constant lighting condition or standard facial expressions, thus cannot deal with both kinds of variations simultaneously. This problem becomes more serious in applications when only one sample images per class is available. In this paper, we present a linear pattern classification algorithm, Adaptive Principal Component Analysis (APCA), which first applies PCA to construct a subspace for image representation; then warps the subspace according to the within-class co-variance and between-class covariance of samples to improve class separability. This technique performed well under variations in lighting conditions. To produce insensitivity to expressions, we rotate the subspace before warping in order to enhance the representativeness of features. This method is evaluated on the Asian Face Image Database. Experiments show that APCA outperforms PCA and other methods in terms of accuracy, robustness and generalization ability.